MND MERFISH Report

1. Overview

  • Project: Human MND MERFISH Analysis

1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

Sample Index and Basic Information
Expt Sample Index Genotype Diagnosis Block RIN Region DataPath Comment
1 H250 H250 Control Control M2 8.0 region_0 Y:/Lab/MERFISH_Imaging_data_2/202407221453_20240722Maize28H250BP1738h271x01_VMSC00101 NA
1 MN28 MN28 MN MNS-ALS/MidAD M2 8.5 region_1 Y:/Lab/MERFISH_Imaging_data_2/202407221453_20240722Maize28H250BP1738h271x01_VMSC00101 NA
2 H260 H260 Control Control M2 8.3 region_0 Y:/Lab/MERFISH_Imaging_data_2/202407221100_20240722105620240722Maize13H260xBP1738x2_VMSC05201 NA
2 MN13 MN13 MN MND-ALS SM2 6.6 region_1 Y:/Lab/MERFISH_Imaging_data_2/202407221100_20240722105620240722Maize13H260xBP1738x2_VMSC05201 Low Cell Detection
3 H264 H264 Control Control M2 8.2 region_0 Y:/Lab/MERFISH_Imaging_data_2/202407261252_20240726MaizeMN32H264BP1738x03_VMSC00101 NA
3 MN32 MN32 MN MND M2 8.3 region_1 Y:/Lab/MERFISH_Imaging_data_2/202407261252_20240726MaizeMN32H264BP1738x03_VMSC00101 NA
4 H262 H262 Control Control/earlyAD M2 4.3 region_0 Y:/Lab/MERFISH_Imaging_data_2/202408051335_20240805MaizeMN40H262BP1738x05_VMSC00101 Low Cell Detection
4 MN40 MN40 MN MND/AD M2 7.8 region_1 Y:/Lab/MERFISH_Imaging_data_2/202408051335_20240805MaizeMN40H262BP1738x05_VMSC00101 NA
5 H250 H250_dup Control Control M2 8.0 region_0 Y:/Lab/MERFISH_Imaging_data_2/202409091144_20240909MaizeMN28H250BP1738m271x04_VMSC00101 NA
5 MN28 MN28_dup MN MNS-ALS/MidAD M2 8.5 region_1 Y:/Lab/MERFISH_Imaging_data_2/202409091144_20240909MaizeMN28H250BP1738m271x04_VMSC00101 NA
6 H246 H246 Control Control M3 7.4 region_0 Y:/Lab/MERFISH_Imaging_data_2/202407261255_20240726MaizeMN31H246BP1738x04_VMSC05201 Bad transcript quality!
6 MN31 MN31 MN MND-ALS M2 9.0 region_1 Y:/Lab/MERFISH_Imaging_data_2/202407261255_20240726MaizeMN31H246BP1738x04_VMSC05201 Bad transcript quality!

1.2 Submission Form

Sample Submission Form

Sample Submission Form

2. Data Quality Control

2.1 MERSCOPE Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

2.1.1 H250

2.1.2 MN28

2.1.3 H260

2.1.4 (Bad Quality!) MN13

2.1.5 H264

2.1.6 MN32

2.1.7 (Bad Quality!) H262

2.1.8 MN40

2.1.9 H250_dup

2.1.10 MN28_dup

2.1.11 (Bad Quality!) H246

2.1.12 (Bad Quality!) MN31

2.2 Bad Sample: Low Cell Count Captured

Cell Detection Comapre on Bad Sample

Cell Detection Comapre on Bad Sample

3. Data Processing & Analysis

3.1 Cell Segmentation & Filtering

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.

3.1.1 Cell Count after Filtering

Cell Total Count After Filtering
id Total
H250 7482
H250_dup 18952
H260 4060
H264 10996
MN28 10582
MN28_dup 24022
MN32 13010
MN40 37891
Total 126995

3.1.2 Transcript Count Violin

Transcript Count Violin After Filtering

Transcript Count Violin After Filtering

3.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of cells and colored by batch

Umap of cells and colored by batch

4. Cell Annotation

We use a recent published tool: Map My Cell to perform cell type annotations for each cell. It is a high resolution cell type annotator build by Alan Institude, with nested levels of classification including 34 classes and 338 subclasses.

The taxonomy is based on the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3)[https://doi.org/10.1016/j.cell.2020.04.007]. Part of the used abbreviations is list in the supplementary Abbreviation. Otherwise can be found in CCFv3 paper.

4.0 Packed glutamatergic cells Cannot be further annotated

It is important to note that in this research, I found it quite challenging to subgroup and identify the glutamatergic cells. According to the UMAP results, these cells appear to be densely packed and indistinguishable from one another, even in spatial map we cannot find their physical position difference, making it difficult to achieve a higher resolution in our work.

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

Umap for only Glutamatergic cells, in fewer umap cluster

4.1 Cell Type Umap

4.2 Cell Type Spatial Map

4.3 Neuron-only Spatial Map

4.3 NonNeuron-only Spatial Map

4.4 Cell Type Count Table

Cell Type Count
id Astrocyte CTX.Glut Endothelial.cells Microglia OPC Oligo Pvalb.Sst.GABA VLMC Vip.GABA Total
H250 365 1914 307 50 37 4308 304 74 123 7482
H250_dup 2017 5495 2296 678 340 5461 1159 838 668 18952
H260 36 2275 39 38 12 1068 460 2 130 4060
H264 1703 5005 472 197 145 1726 1088 90 570 10996
MN28 884 4540 546 194 91 3839 323 38 127 10582
MN28_dup 2732 7469 2014 1062 580 8483 822 443 417 24022
MN32 961 5446 501 112 116 4299 1076 56 443 13010
MN40 5835 4319 1864 3536 477 19154 919 1325 462 37891
Total 14533 36463 8039 5867 1798 48338 6151 2866 2940 126995

5. Gene differentiation expresssion (MND v.s. Control)

Here, we use Pseudobulk to compute gene differential expression (DE). The statistical significance was cut-off by log2(Fold Change) > 1 or log2(Fold Change) < -1 and p_value < 0.05.

The result are visualized via Volcano plot: a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change).

The comparison is between Human MND and Control samples. A positive fold change means higher expression in the MND group.

5.1 all

X baseMean log2FoldChange lfcSE stat pvalue padj
NA NA NA NA NA NA NA
:-: :——–: :————–: :—–: :—-: :——: :—-:

5.2 Astrocyte

X baseMean log2FoldChange lfcSE stat pvalue padj
COL6A2 162.98309 -2.187921 0.4205553 -5.202456 0.0000002 0.0000533
FAM189A2 203.22058 -1.350657 0.3920381 -3.445219 0.0005706 0.0515438
ACSS1 2240.78726 1.249025 0.3467052 3.602556 0.0003151 0.0426964
COLEC12 29.87578 -1.140547 0.5656182 -2.016462 0.0437517 0.9860140
ANXA11 415.67795 -1.138904 0.3452568 -3.298715 0.0009713 0.0658046

5.3 CTX Glut

X baseMean log2FoldChange lfcSE stat pvalue padj
NA NA NA NA NA NA NA
:-: :——–: :————–: :—–: :—-: :——: :—-:

5.4 Endothelial cells

X baseMean log2FoldChange lfcSE stat pvalue padj
UBE2L6 55.27175 1.894800 0.4673114 4.054684 0.0000502 0.0130024
PLOD2 42.91774 -1.218829 0.5483994 -2.222520 0.0262482 0.7553648
LHX6 44.36169 -1.176930 0.4854181 -2.424569 0.0153266 0.4961978
AGT 69.17581 -1.107489 0.4058137 -2.729056 0.0063516 0.3290121

5.5 Microglia

X baseMean log2FoldChange lfcSE stat pvalue padj
CX3CR1 38.45987 1.919454 0.6508097 2.949331 0.0031846 0.8120784
VIPR1 20.03801 1.349306 0.6774270 1.991810 0.0463919 0.9999443

5.6 Oligo

X baseMean log2FoldChange lfcSE stat pvalue padj
AGT 986.5454 -1.780771 0.2343143 -7.599922 0 0

5.7 OPC

X baseMean log2FoldChange lfcSE stat pvalue padj
NRN1 7.276883 2.170530 0.894654 2.426111 0.0152616 0.9933805
AGT 92.744807 -1.463884 0.651756 -2.246062 0.0247001 0.9933805

5.8 Pvalb_Sst GABA

X baseMean log2FoldChange lfcSE stat pvalue padj
HTR1B 5.753365 -2.008969 0.9880846 -2.033196 0.0420328 0.9986592

5.9 Vip GABA

X baseMean log2FoldChange lfcSE stat pvalue padj
MAG 107.12333 -1.611034 0.3151147 -5.112533 0.0000003 0.0000823
COL6A2 23.25742 -1.412148 0.5735480 -2.462127 0.0138116 0.4398683
MOG 41.83839 -1.370855 0.4033419 -3.398741 0.0006770 0.0438337
MYRF 64.57717 -1.281464 0.3401469 -3.767385 0.0001650 0.0142421
TTYH2 25.73642 -1.009652 0.5109021 -1.976214 0.0481306 0.8904153

5.10 VLMC

X baseMean log2FoldChange lfcSE stat pvalue padj
SLC6A1 190.848205 2.354861 0.5465104 4.308905 0.0000164 0.0018211
AGT 299.781119 -2.300196 0.4383285 -5.247654 0.0000002 0.0000342
UBE2L6 8.715843 2.014432 0.9885191 2.037828 0.0415672 0.9769378
ITIH5 83.347303 1.949393 0.5207117 3.743709 0.0001813 0.0134179
PDE3A 58.668776 -1.732942 0.5199404 -3.332963 0.0008593 0.0476891
IRAG1 42.821461 1.505937 0.5828785 2.583620 0.0097769 0.4340960

6. TDP Gene: ANXA11

DE analysis revealed that one of the TDP genes, ANXA11, exhibits an interesting expression pattern: there is no significant expression difference across all cell comparisons, but it is up-regulated specifically in Astrocyte and Vip_GABA cells.

All p value in this part was calculated via Wilcoxon rank-sum statistic.

6.1 ANXA11 in Astrocyte cells

6.2 ANXA11 in Vip_GABA cells

6.3 ANXA11 Spatial Map

6.4 Other TDP Genes

Supplement 2: Abbreviation

Cell types & Regions

Ast, Astrocyte;

ABC, arachnoid barrier cells;

BAM, border-associated macrophages;

BLA, Basolateral amygdala;

CB, cerebellum;

CGE, caudal ganglionic eminence;

CHOR, choroid plexus;

CNU, cerebral nuclei;

CR, Cajal–Retzius;

CT, corticothalamic;

CTX, cerebral cortex;

CTXsp, cortical subplate;

DC, dendritic cells;

DCO, dorsal cochlear nucleus;

DG, dentate gyrus;

EA, extended amygdala;

Endo, endothelial cells;

ENT, Entorhinal area;

ENTl, Entorhinal area, lateral part;

Epen, ependymal;

EPI, epithalamus;

ET, extratelencephalic;

GC, granule cell;

HB, hindbrain;

HPF, hippocampal formation;

HY, hypothalamus;

HYa, anterior hypothalamic;

IMN, immature neurons;

IT, intratelencephalic;

L6b, layer 6b;

LGE, lateral ganglionic eminence;

LH, lateral habenula;

LSX, lateral septal complex;

MB, midbrain;

MGE, medial ganglionic eminence;

MH, medial habenula;

MM, medial mammillary nucleus;

MY, medulla;

NN, non-neuronal;

NP, near-projecting;

NT, non-telencephalon;

OB, olfactory bulb;

OEC, olfactory ensheathing cells;

OLF, olfactory areas;

Oligo, oligodendrocytes;

OPC, oligodendrocyte precursor cells;

P, pons;

PAL, pallidum;

Peri, pericytes;

PIR, piriform cortex;

SMC, smooth muscle cells;

STR, striatum;

TE, telencephalon;

TH, thalamus;

UBC, unipolar brush cells;

VLMC, vascular leptomeningeal cells.

Neurotransmitter types

Chol, cholinergic;

Dopa, dopaminergic;

GABA, GABAergic;

Glut, glutamatergic;

Glyc, glycinergic;

Hist, histaminergic;

Nora, noradrenergic;

Sero, serotonergic

ADP, anterodorsal preoptic nucleus

AHN, anterior hypothalamic nucleus

ARH, arcuate hypothalamic nucleus

CLI, central linear nucleus raphe

CUN, cuneiform nucleus

DMH, dorsomedial nucleus of the hypothalamus

DMX, dorsal motor nucleus of the vagus nerve

IF, interfascicular nucleus raphe

LHA, lateral hypothalamic area

MDRN, medullary reticular nucleus

MPN, medial preoptic nucleus

MPO, medial preoptic area

MV, medial vestibular nucleus

NTS, nucleus of the solitary tract

PAG, periaqueductal grey

PARN, parvicellular reticular nucleus

PB, parabrachial nucleus

PBG, parabigeminal nucleus

PGRN, paragigantocellular reticular nucleus

PGRNd, paragigantocellular reticular nucleus, dorsal part

PH, posterior hypothalamic nucleus

PMv, ventral premammillary nucleus

PPN, pedunculopontine nucleus

PVa, periventricular hypothalamic nucleus, anterior part

PVHd, paraventricular hypothalamic nucleus, descending division

PVi, periventricular hypothalamic nucleus, intermediate part

PVpo, periventricular hypothalamic nucleus, preoptic part

PVR, periventricular region

RAmb, midbrain raphe nuclei

RL, rostral linear nucleus raphe

SBPV, subparaventricular zone

SNc, substantia nigra, compact part

SPIV, spinal vestibular nucleus

TMv, tuberomammillary nucleus, ventral part

VII, facial motor nucleus

VMPO, ventromedial preoptic nucleus

VTA, ventral tegmental area

ZI, zona incerta.